Method for diagnosing multiple sclerosis

ABSTRACT

Disclosed is a method for diagnosing multiple sclerosis and more particularly to a method for diagnosing multiple sclerosis by measuring levels of antibodies to glycans in a biological sample.

FIELD OF THE INVENTION

The invention relates generally to a method and reagents for diagnosing, and assessing the prognosis of, multiple sclerosis and more particularly to a method and reagents for diagnosing, and assessing the prognosis of, multiple sclerosis by measuring levels of antibodies to glycans in a biological sample.

BACKGROUND OF THE INVENTION

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system. It is a common cause of persistent disability in young adults. In patients suffering from MS, the immune system destroys the myelin sheet of axons in the brain and the spinal chord, causing a variety of neurological pathologies. In the most common form of MS, Relapsing-Remitting, episodes of acute worsening of neurological function (exacerbations, attacks) are followed by partial or complete recovery periods (remissions) that are free of disease progression (stable). It has been reported that ninety percent of patients with MS initially present with a clinically isolated syndrome because of an inflammatory demyelinating lesion in the optic nerve, brain stem, or spinal cord. About thirty percent of those patients with a clinically isolated syndrome progress to clinically definite MS within 12 months of presentation. The subsequent progression of the disease can vary significantly from patient to patient. The progression can range from a benign course to a classic relapsing—remitting, chronic progressive, or rare fulminant course. A method for diagnosing MS that facilitates early MS diagnosis and prediction of disease activity (Benign, Moderate and Malignant) would be valuable for both managing the disease and providing counsel for the patient. For example, patients diagnosed early with active course of MS could be offered disease modifying treatments that have recently been shown to be beneficial in early MS.

Current methods for assessment and tracking progress of MS are based on assessment and scoring of patients' function in attacks and accumulated disabilities during the attacks. One assessment used to assess MS is the Expanded Disability Status Scale (EDSS). However, EDSS score system measures the outcome and does not have predict for the progression of the disease. In addition, EDSS scoring can be variable because it is based on a subjective assessment of patient function. Methods for diagnosis can also include tracking brain lesions by Magnetic Resonance Imaging (MRI) or testing Cerebrospinal Fluid (CSF) for Oligo-Clonal Banding (OCB). MRI is a physical method for assessment of brain lesions and is used widely for MS diagnosis. However, it has only very long term predictive value. In addition, the correlation between MRI results and disease activity is poor. Thus, MRI can not be used for short term projections of disease activity or disease management.

Cerebrospinal puncture is an unpleasant invasive procedure that is not suitable for routine use or prognosis. In addition, both methods assess damage only after it has occurred; neither method can predict the onset of attacks or silent, sub-clinical lesions. A further disadvantage in testing for OCB in CSF and MRI as a way to diagnose MS is that a negative OCB or MRI will not preclude the existence of MS.

Most patients with MS initially present with a clinically isolated syndrome (CIS). Despite the fact that MS will develop in up to 80% of these patients, the course of the disease is unpredictable at its onset. The disease may remain inactive for many years before the appearance of a second clinical relapse or new lesions on MRI confirm the diagnosis. Because currently available therapy is only partially effective and side effects are common, many neurologists are uncertain whether to treat all such patients with immunomodulators, or to wait until the diagnosis is confirmed by a second clinical event or the appearance of new MRI lesions.

There is a need for a simple serological assay that predicts whether patients with a CIS suggestive of MS or newly diagnosed relapsing remitting MS will have a highly active disease course and therefore require aggressive treatment, or whether they will follow a more benign course that enables such patients to postpone immunomodulatory therapy until necessary. This assay would be also useful in helping the diagnosis of MS.

There is also a need for a method that uses objectively assessed markers for diagnosing MS and for predicting disease activity, the onset of attacks or silent lesions in patients suffering from MS.

SUMMARY OF THE INVENTION

The invention is based in part on the discovery that MS patients have higher serum levels of IgM antibodies that bind the glycan structures Glc(α1,2)Glc(α) or Glc(α1,3)Glc(α) or Glc(α1,6)Glc(α) as compared to the serum levels of these antibodies in individuals with other neurological diseases (chronic, inflammatory or non-inflammatory). The levels of IgM anti Glc(α1,2)Glc(α) or Glc(α1,3)Glc(α) or Glc(α1,6)Glc(α) in serum can act as a staging and prognostic marker for the activity of the disease. Levels of the antibodies can also be used to decide on treatment and to track the efficacy of treatment.

Measuring the levels of these antibodies in the blood of MS suspected patients facilitates quick and cost effective early diagnosis of MS patients, disease activity prediction and early prescribing of disease modifying drugs. Monitoring of the levels of those antibodies in the blood of defined MS patients also allows for quick and cost effective monitoring of the effects of prescribed drugs, and early detection of attacks or sub-clinical silent lesions, enabling better treatment.

Among the additional advantages of the invention are that the existence of MS in patients can be determined at an earlier stage of the disease, when its symptoms may resemble many other MS-like diseases or when the symptoms are still not sufficient to finally define the patient as having MS. Early diagnosis allows physicians to treat MS earlier in the course of the disease, thereby minimizing or preventing the damage caused by the destruction of myelin and disabilities brought about by this destruction. In addition, the methods disclosed herein enable physicians to follow MS patients regularly in order to assess the disease activity, to monitor therapy, and change treatment once signs for coming attacks appear. For example, an increase in biomarkers indicative of an MS attack may warrant administration to the patient of methylpredisone, which is a general anti inflammatory agent commonly administered during attacks.

In one aspect, the invention features a method of diagnosing, or assessing the prognosis of, multiple sclerosis in a subject. The method includes providing a test sample from a subject and detecting in the test sample at least one biomarker that is an antibody that binds specifically to a glycan structure. The antibody can be, e.g., anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc((α) antibody or anti-Glc(α1,6)Glc(α) antibody. The levels of antibody or antibodies in the test sample are compared to a control sample, which is derived from one or more individuals who have multiple sclerosis symptoms and have a known multiple sclerosis status, or from an individual or individuals who do not show multiple sclerosis symptoms. MS status can include, e.g., exacerbations, attacks, remissions, benign, moderate, malignant and stable stages of the disease.

In various embodiments, at least 1, 2 or 3 of these antibodies are detected.

In some embodiments, the method further comprises detecting one or more additional antibodies. The antibody can be, e.g., an anti α-Glc antibody (including an anti α-Glc IgM antibody), an anti-Glc(α1,4)Glc(α) antibody (including an anti-Glc(α1,4)Glc(α) IgM antibody), an anti α-GlcNAc antibody (including an anti α-GlcNAc IgM antibody), an anti β-GlcNAc antibody, an anti-Glc(α1,4)Glc(β) antibody an anti β-Glc antibody, an anti β-Gal antibody, an anti-Glc(α1,4)Glc(β1,4)Glc(β) antibody, an anti-GlcNAc(β,1,4)GlcNAc(β) antibody, an anti α-L-Araf antibody, an anti α-L-Rha antibody, an anti-Gal (β1,3)[GlcNAc(β1,6)]GalNAc(α) antibody, an anti-Gal(β1,4)GlcNAc(α) antibody, an anti-Gal(β1,3)GalNAc(α), an anti-Gal(β,1-3)GlcNAc(β), an anti β-GlcA antibody or an anti α-Xyl antibody. In various embodiments, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18 of these additional antibodies are detected.

In some embodiments, the control sample consists essentially of a population of one or more individuals that do not show symptoms of a multiple sclerosis and do not have multiple sclerosis. In other embodiments, the control sample consists essentially of a population who do show symptoms of a multiple sclerosis and do have multiple sclerosis. In other embodiments, the control sample consists essentially of a population of one or more individuals with neurological diseases other then multiple sclerosis. In other embodiments, the control sample consists essentially of a population of one or more individuals with autoimmune diseases other then multiple sclerosis.

The presence of MS in the control sample can be determined using techniques known in the art, e.g., Clinical neurological examination, or an Expanded Disability Status Scale (EDSS) assessment, Magnetic Resonance Imaging (MRI) assessment, or testing for OCB in the CSF or combination of some or all of the techniques.

The test sample can be, e.g., a biological fluid. Examples of biological fluids include, e.g., whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva.

The subject can be either a female or a male.

The antibody detected can be, e.g., an IgM type or an IgA type or an IgG antibody.

In some embodiments, the type of multiple sclerosis detected is early multiple sclerosis.

Also provided by the invention is a method of diagnosing a multiple sclerosis exacerbation in a subject. The method includes providing a test sample from a subject and detecting an anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody in the test sample. The levels of the antibody in the test sample are compared to a control sample, which is derived from one or more individuals whose multiple sclerosis status is known.

In some embodiments, the control sample consists essentially of a population of one or more individuals that do not show symptoms of a multiple sclerosis exacerbation and whose multiple sclerosis status is in remission. A multiple sclerosis exacerbation is diagnosed in the subject if more anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody is present in the test sample than in the control sample. In other embodiments, the control sample consists essentially of a population of one or more individuals that show symptoms of a multiple sclerosis exacerbation, and a multiple sclerosis exacerbation is diagnosed in the subject if levels of anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody are present in similar amounts in the test sample and the control sample.

The test sample can be, e.g., a biological fluid. Examples of biological fluids include, e.g., whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva.

The subject can be either a female or a male.

The antibody detected can be, e.g., an IgM type, an IgA or an IgG type antibody.

In some embodiments, the diagnosis is an early diagnosis of multiple sclerosis exacerbation.

In some embodiments, the subject has been treated with an MS therapeutic agent, e.g., interferon beta or glitamerer acetate administered subcutaneously.

Also within the invention is method for assessing multiple sclerosis disease activity in a subject. The method includes providing a test sample from a subject and determining whether the test sample contains an anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc((α1,6)Glc(α) antibody. The amount of antibody in the test sample is compared to the amount of the antibody in the control sample, which is derived from one or more individuals whose multiple sclerosis disease activity is known.

In some embodiments, the control sample consists essentially of a population of one or more individuals whose multiple sclerosis disease activity is defined by Expanded Disability Status Scale (EDSS), changes in an EDSS score, frequency of relapses or a Magnetic Resonance Imaging (MRI) assessment.

The test sample can be, e.g., a biological fluid. Examples of biological fluids include, e.g., whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva. If desired, the method may further include selecting a therapeutic agent for treating multiple sclerosis by selecting a therapeutic agent and dosage regimen based on the relative levels of the antibody or antibodies in the test sample and the control sample.

In some embodiments, higher levels of antibodies in the test sample relative to the control sample indicate selection of a therapeutic agent and dosage regimen that is subcutaneous administration of interferon beta (BETAFERON®, AVONEX®, REBIF®) or subcutaneous administration of glitamerer acetate (COPAXONE®).

The subject can be either a female or a male.

In a further aspect, the invention provides a method of selecting a therapeutic agent for treating multiple sclerosis. The method includes providing a test sample from a subject diagnosed with, or at risk for, multiple sclerosis and determining whether the test sample contains anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(a) antibody or anti-Glc(α1,6)Glc(α) antibody. Levels of the antibody in the test sample to are compared to levels of antibody in a control sample consisting essentially of one or more individuals whose multiple sclerosis disease activity is known. A therapeutic agent and dosage regimen is selected based on the relative levels of the antibody in the subject sample and the control sample.

In some embodiments, the method further includes determining whether the test sample contains anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody and comparing the levels of anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody in the test sample to levels of antibody in a control sample consisting essentially of one or more individuals whose multiple sclerosis disease severity is known.

In some embodiments, the control sample consists essentially of one or more individuals whose status is no multiple sclerosis or stable multiple sclerosis.

In a further aspect, the invention provides a method to predict whether patients with a CIS suggestive of MS or newly diagnosed relapsing remitting MS will have a highly active disease course and therefore require aggressive treatment, or whether they will follow a more benign course that enables such patients to postpone immunomodulatory therapy until necessary.

The method includes providing a test sample from a subject diagnosed with, or at risk for, multiple sclerosis and determining whether the test sample contains anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc((α1,6)Glc(α) antibody. Levels of the antibody in the test sample are compared to levels of antibody in a control sample consisting essentially of one or more individuals whose multiple sclerosis disease activity and course is known. A therapeutic agent and dosage regimen is selected based on the relative levels of the antibody in the subject sample and the control sample.

Also provided by the invention is a kit for diagnosing and predicting disease activity associated with multiple sclerosis. The kit includes a first reagent that specifically detects anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody, and a second reagent that specifically detects specifically detects a second antibody selected from the group consisting of an anti α-Glc antibody (including an anti α-Glc IgM antibody), an anti-Glc(α1,4)Glc(α) antibody (including an anti-Glc(α1,4)Glc(α) IgM antibody), an anti α-GlcNAc antibody (including an anti α-GlcNAc IgM antibody), an anti β-GlcNAc antibody, an anti-Glc(α1,4)Glc(β) antibody an anti β-Glc antibody, an anti β-Gal antibody, an anti-Glc(β1,4)Glc(β1,4)Glc(β) antibody, an anti-GlcNAc(β1,4)GlcNAc(p) antibody, an anti α-L-Araf antibody, an anti α-L-Rha antibody, an anti-Gal (β1,3)[GlcNAc(β1,6)]GalNAc(α) antibody, an anti-Gal(β1,4)GlcNAc(α) antibody, an anti-Gal(β1,3)GalNAc(α), an anti-Gal(β,1-3)GlcNAc(β), an anti β-GlcA antibody and an anti α-Xyl antibody. The kit may include one or all reagents, and directions for using the kit. The kit optionally includes a reagent that specifically detects an IgM type antibody.

Also within the invention are substrates that include reagents that specifically detect the antibodies disclosed herein, e.g., anti-Glc(α1,2)Glc(α) antibody or anti-Glc((α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody. The invention also includes reagents that specifically detect antibodies used in combination with the antibodies disclosed above, e.g., an anti α-Glc antibody (including an anti α-Glc IgM antibody), an anti-Glc(α1,4)Glc(α) antibody (including an anti-Glc(α1,4)Glc(α) IgM antibody), an anti α-GlcNAc antibody (including an anti α-GlcNAc IgM antibody), an anti β-GlcNAc antibody, an anti-Glc(β1,4)Glc(β) antibody an anti β-Glc antibody, an anti β-Gal antibody, an anti-Glc(β1,4)Glc(β1,4)Glc(β) antibody, an anti-GlcNAc(β1,4)GlcNAc(β) antibody, an anti α-L-Araf antibody, an anti α-L-Rha antibody, an anti-Gal (β1,3)[GlcNAc(β1,6)]GalNAc(α) antibody, an anti-Gal(β1,4)GlcNAc(α) antibody, an anti-Gal(β1,3)GalNAc(α), an anti-Gal(β1-3)GlcNAc(β), an anti β-GlcA antibody or an anti α-Xyl antibody. The substrate can be, e.g., planar. In a further aspect, the reagents may be connected to a substrate via a linker.

Also within the invention are reagents for diagnosing and predicting disease activity associated with multiple sclerosis, that specifically detects anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody or all. In a further aspect, the reagents may be connected to a substrate via a linker. The substrate may be a bead particles or a planer substrate.

In some embodiments, the reagents that are used to specifically bind and detect those anti glycans antibodies are the specific glycan structures. In other embodiments, the reagents are other molecules or macromolecules that include the specific glycan structure. For example, the anti-Glc(α1,2)Glc(α) antibody can be detected using a polymer of glucose units connected with one or more Glc(α1,2)Glc(α) glycosidic bonds. Thus, the glycan itself can be used for detecting the corresponding antibody or antibodies, as can any carbohydrate, peptide, protein, or any other molecular structure that includes the glycan.

In some embodiments, the reagents that are used to specifically bind and detect the anti glycans antibodies of the invention are peptides that mimic the carbohydrate antigens of the invention. The peptides can be used to identify specific anti glycan antibodies.

In some embodiment peptides that mimics the specific carbohydrates of this invention can be used for identification of the specific anti glycan antibodies. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patent, and other references mentioned herein are incorporated by reference in their entirety. In the case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph showing Receiver Operator Characteristic (ROC) curves for differentiation between MS and OND patients using level of IgM antibodies against Glc(α1,2)Glc(α)-(Ga2Ga), Glc(α1,3)Glc(α)-Ga3Ga, Glc((α1,6)Glc(α)-Ga6Ga, Glc(α1,4)Glc(α)-Ga4Ga, α-Glc-Ga, α-GlcNAc-GNa.

DETAILED DESCRIPTION OF THE INVENTION

The methods provided herein allow for early diagnosis of initial and recurring multiple sclerosis, as well as prediction of disease activity (benign, moderate and malignant), using objectively assessed biomarker levels.

A patient with acute worsening of neurological function initially has to be diagnosed as a defined MS patient before being eligible for treatment with disease modifying drugs. The physician will have to determine if the patient has MS like symptoms (such as Younger stroke, Lupus, Vitamin B-12 deficiency, Anti phospholipid syndrome, Severe Migraine) or if they actually have MS. The patient will have to experience a second acute worsening of neurological function (attack) before being diagnosed as a MS patient and be able to start chronic treatment with a MS therapeutic agent such as interferon beta or glatiramer acetate.

Currently, physicians are using MRI for the identification of the existence of brain lesions and/or the testing of Cerebrospinal Fluid (CSF) for Oligo Clonal Banding (OCB). If MRI gives a clear result regarding the existence of brain lesions or the presence of OCB in the CSF, the physician may start treatment immediately in order to prevent silent brain lesions. A diagnosis of full MS diagnosis is currently made only after the second attack or the appearance new MRI finding with dissemination in time and space. In case MRI does not give a clear result or there are no OCB in the patients CSF, no MS is diagnosed and treatment is delayed until following a second attack (McDonald et al., Ann Neurol. 50:121-27, 2001).

Most patients with MS initially present with a clinically isolated syndrome (CIS). Despite the fact that MS will develop in up to 80% of these patients, the course of the disease is unpredictable at its onset. The disease may remain inactive for many years before the appearance of a second clinical relapse or new lesions on MRI confirm the diagnosis. Because currently available therapy is only partially effective and side effects are common, many neurologists are uncertain whether to treat all such patients with immunomodulators, or to wait until the diagnosis is confirmed by a second clinical event or the appearance of new MRI lesions. This invention provides a simple serological assay to predict whether patients with a CIS suggestive of MS or newly diagnosed relapsing remitting MS will have a highly active disease course and therefore require aggressive treatment, or whether they will follow a more benign course that enables such patients to postpone immunomodulatory therapy until necessary. This assay is also useful for helping diagnosing MS.

The methods disclosed herein can be performed by extracting blood from a patient with acute worsening of neurological function and suspected to have MS or an already defined RRMS patient. The method can identify the existence of MS and to predict the up coming course of the diseases by measuring anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody levels. If the level of at least one of these antibodies is significantly higher then the average level of these antibodies in sera of healthy individuals, patients with neurological diseases other then MS, or patients with autoimmune diseases other then MS, the patient is diagnosed as an MS patient without the need to wait for a second attack or for further MRI findings. In addition, the quick diagnosis allows for treatment to begin immediately.

Screening the patient's blood and determining the level of biomarkers disclosed herein, anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody, allows for accurate monitoring of therapy. For example, one first line of treatment for MS is interferon β (e.g., INFβ-1a and INFβ-1b). The current evaluation of effectiveness and required dosage of the drug is based on continued monitoring of several clinical scores. Currently, the EDSS score and its change over time (e.g., by comparing the difference in the EDSS every 3-6 months) is the main clinical parameter for disease management. An important component of the assessment is the level of fatigue and depression experienced by the patient. The fatigue and or depression can be a symptom of MS, as an autoimmune disease, or a side effect from the usage of interferon beta. Identifying the cause of the fatigue is important for managing the treatment. For example, if the fatigue is a result of a side effect of the interferon, the physician will consider lowering the dosage or even exchanging it for another drug. However, if the fatigue is due to the MS symptoms, the physician will have to consider increasing the drug dosage. Significant decreases in antibody levels indicate that the patient is responding well to the given drug.

Currently there is no way to predict the onset of attacks and sub-clinical silent lesions in MS patients. MRI and clinical evaluation of the patients can only reveal damage that has already occurred. Periodical measurement of the level of a few anti glycan antibodies (for example, anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody) in the patient's blood according to the method described herein allows for physicians to predict the upcoming disease activity (e.g. frequency of clinical attacks and appearance of sub clinical MRI silent lesions) based upon an increase in levels of these antibodies.

All the glycan structures that are discussed in this disclosure, unless mentioned otherwise are connected to in the indicated anomericity α or β through linker to solid phase.

In some embodiment peptides that mimics the specific glycans of this invention can be used for identification of the specific anti glycan antibodies. Those peptide that mimics carbohydrate can be identifies for example from screening a filamentous phage-displayed random peptide library (Zhan et al., Biochem Biophys Res Commun. 308:19-22, 2003; Hou et al., J Immunol. 170:4373-79, 2003).

Most patients with Multiple Sclerosis (MS) initially present with a clinically isolated syndrome (CIS). Despite the fact that clinically definite MS will develop in up to 80% of these patients, the course of the disease is unpredictable at its onset. The disease may remain inactive for many years before the appearance of a second clinical relapse or new lesions on MRI confirm the diagnosis. Because currently available therapy is only partially effective and side effects are common, many neurologists are uncertain whether to treat all such patients with immunomodulators, or to wait until the diagnosis is confirmed by a second clinical event or the appearance of new MRI lesions.

The invention provides a simple serological assay that may be used to predict whether patients with a CIS suggestive of MS or newly diagnosed relapsing remitting MS will have a highly active disease course and therefore require aggressive treatment, or whether they will follow a more benign course that enables such patients to postpone immunomodulatory therapy until necessary.

The invention additionally provides a simple serological test for the definite confirmation of MS and of the level of the risk in individuals presenting a primary acute demyelinating event. Ninety percent of patients with MS initially present with a clinically isolated syndrome due to an inflammatory demyelinating lesion in the optic nerve, brain stem, or spinal cord (O'Riordan et al., Brain 121: 495-503, 1998). Thirty percent of these patients with clinically isolated syndrome will have progression to definite multiple sclerosis within 12 month after presentation (Brex et al., N. Engl. J. Med. 346:158-164, 2002; O'Riordan et al., Brain 121: 495-503, 1998; Jacobs et al., Ann. Neurol. 41:392-98, 1997), but no more than 80% of patients with a clinically primary event will develop clinically definite MS (Weinshenker et al., Brain 112:1419-28, 1989). Thus, it is desirable to unambiguously confirm and stage MS prior to commencing treatment with disease modifying drugs.

The methods can be used to determine whether a particular treatment MS treatment regimen is warranted for a particular patient. Patients at high risk for rapid progression to definite MS can be offered disease-modifying treatments that have recently been shown to be beneficial in early multiple sclerosis (Comi et al., Lancet 351:1576-82, 2001; Jacobs et al., N. Engl. J. Med. 343:898-904, 2000). On the other hand, for patients at low risk, and who have a chance of remaining relapse free for several years after an initial demyelinating event, immunomodulatory therapy might be postponed until necessary. Thus, an advantage of the invention is better disease management at the early days of the disease.

The presence of antibodies to Glc(α1,2)Glc(α), Glc(α1,3)Glc(α) and/or Glc(α1,6)Glc(α) can be combined with other diagnostic tests for diagnosing multiple sclerosis. One such test is the MS-associated antibodies disclosed in WO 2004/015420, the contents of which are incorporated by reference in their entirety. These antibodies include, e.g., an anti α-Glc antibody (including an anti α-Glc IgM antibody), an anti-Glc(α1,4)Glc(α) antibody (including an anti-Glc(α1,4)Glc(α) IgM antibody), an anti α-GlcNAc antibody (including an anti α-GlcNAc IgM antibody), an anti β-GlcNAc antibody, an anti-Glc(α1,4)Glc(β) antibody an anti β-Glc antibody, an anti β-Gal antibody, an anti-Glc(β1,4)Glc(β1,4)Glc(β) antibody, an anti-GlcNAc(β,1,4)GlcNAc(β) antibody, an anti α-L-Araf antibody, an anti α-L-Rha antibody, an anti-Gal(β1,3)[GlcNAc(β1,6)]GalNAc(α) antibody, an anti-Gal(β1,4)GlcNAc(α) antibody, an anti-Gal(β1,3)GalNAc(α), an anti-Gal(β,1-3)GlcNAc(β), an anti β-GlcA antibody or an anti α-Xyl antibody.

The methods are typically performed using reagents that specifically bind to the anti-glycan antibodies. The reagents can be, e.g., the specific glycan structures. Alternatively, the reagents can be other molecules or macromolecules that include the specific glycan structure. For example, the Glc(α1,2)Glc(α) antibody can be detected using a polysaccharide that includes a polymer with one or more Glc(α1,2)Glc(α) linkages. Thus, the glycan itself can be used for detecting the corresponding antibody or antibodies, as can any carbohydrate, peptide, protein, or any other molecular structure that includes the glycan.

If desired, peptides that mimic carbohydrate antigens can be used in the methods and compositions described herein. The peptides can be used to identify specific anti glycan antibodies. Peptides which mimic structures recognized by antiglycan antibodies can be identified using methods known in the art, e.g., by screening a filamentous phage-displayed random peptide library (Zhan et al., Biochem Biophys Res Commun. 308:19-22, 2003; Hou et al., J Immunol. 17:4373-79, 2003).

Glycan antigens used to identify various anti-glycan antibodies can be obtained from a variety of other sources so long as the antigen is capable of binding specifically to the given anti-glycan Binding to anti-glycan antibodies can be performed using variety of other immunoassay formats known in the art, including competitive and non-competitive immunoassay formats can also be used (Self and Cook, Curr. Opin. Biotechnol. 7:60-65 (1996), which is incorporated by reference). Other assays include immunoassays, such as enzyme-linked immunosorbent assays (ELISAs). An enzyme such as horseradish peroxidase (HRP), alkaline phosphatase (AP), β-galactosidase or urease can be linked to a secondary antibody selective for a primary anti-glycan antibody of interest. A horseradish-peroxidase detection system can be used, for example, with the chromogenic substrate tetramethylbenzidine (TMB), which yields a soluble product in the presence of hydrogen peroxide that is detectable at 450 nm. An alkaline phosphatase detection system can be used with the chromogenic substrate p-nitrophenyl phosphate, for example, which yields a soluble product readily detectable at 405 nm. Similarly, a β-galactosidase detection system can be used with the chromogenic substrate o-nitrophenyl-aβ-D-galactopyranoside (ONPG), which yields a soluble product detectable at 410 nm, or a urease detection system can be used with a substrate such as urea-bromocresol purple (Sigma Immunochemicals, St. Louis, Mo.). A useful secondary antibody linked to an enzyme can be obtained from a number of commercial sources; goat F(ab′)₂ anti-human IgG-alkaline phosphatase, for example, can be purchased from Jackson Immuno-Research (West Grove, Pa.);

Immunoassays encompass capillary electrophoresis based immunoassays (CEIA) and can be automated, if desired. Immunoassays also can be used in conjunction with laser induced fluorescence (see, for example, Schmalzing and Nashabeh, Electrophoresis 18:2184-93 (1997)); Bao, J. Chromatogr. B. Biomed. Sci. 699:463-80 (1997), each of which is incorporated herein by reference). Liposome immunoassays, such as flow-injection liposome immunoassays and liposome immunosensors, also can be used (Rongen et al., J. Immunol. Methods 204:105-133 (1997)).

A radioimmunoassay can also be used for determining whether a sample is positive for a glycan antibody, or for determining the level of anti-glycan antibodies in a sample. A radioimmunoassay using, for example, an ¹²⁵Iodine-labeled secondary antibody (Harlow and Lane, Antibodies A Laboratory Manual Cold Spring Harbor Laboratory: New York, 1988, which is incorporated herein by reference) is encompassed within the invention.

A secondary antibody may alternatively be labeled with a chemiluminescent marker. Such a chemiluminescent secondary antibody is convenient for sensitive, non-radioactive detection of anti-glycan antibodies and can be obtained commercially from various sources such as Amersham Lifesciences, Inc. (Arlington Heights, Ill.).

A detectable reagent may also be labeled with a fluorochrome. Appropriate fluorochromes include, for example, DAPI, fluorescein, Hoechst. 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas red or lissamine. A particularly useful fluorochrome is fluorescein or rhodamine. Secondary antibodies linked to fluorochromes can be obtained commercially. For example, goat F(ab′)₂ anti-human IgG-FITC is available from Tago Immunologicals (Burlingame, Calif.).

A signal from the detectable reagent can be analyzed, for example, using a spectrophotometer to detect color from a chromogenic substrate; a radiation counter to detect radiation, such as a gamma counter for detection of ¹²⁵Iodine; or a fluorometer to detect fluorescence in the presence of light of a certain wavelength. For detection of enzyme-linked reagents, a quantitative analysis of the amount of anti-glycan antibodies can be made using a spectrophotometer such as an EMAX Microplate Reader (Molecular Devices, Menlo Park, Calif.) in accordance with the manufacturer's instructions. If desired, the assays of the invention can be automated or performed robotically, and the signal from multiple samples can be detected simultaneously.

Other methods include, e.g., flow cytometry (including bead based immunoassays), and phage display technology for expressing a recombinant antigen specific for an anti-glycan antibody. Phage particles expressing the antigen specific for a desired anti-glycan antibody can be anchored, if desired, to a multiwell plate using an antibody such as an anti phage monoclonal antibody (Felici et al., “Phage-Displayed Peptides as Tools for Characterization of Human Sera” in Abelson (Ed.), Methods in Enzymol. 267, San Diego: Academic Press, Inc. (1996), which is incorporated by reference herein).

The invention will be illustrated in the following non-limiting examples.

EXAMPLE 1

Recruitment of Patients

One hundred and fifteen (115) Outpatients aged 18-55 years with clinically definite and laboratory-supported MS, according to the Poser criteria (Poser et al., Ann Neurol 13, 227-231, 1883), were eligible for the study. Inclusion criteria for patients with RRMS were a history of at least two clearly identified and documented relapses in the 2 years prior to study entry, and being ambulant, defined by Kurtzke's expanded disability status scale (EDSS) (Kurtzke, Neurology 33, 1444-1452, 1983) of 0-6.5. Relapse was defined as the appearance or reappearance of one or more neurological abnormalities that persisted for at least 24 h, and which had been preceded by at least 30 days of stable or improved neurological state. Exclusion criteria were corticosteroids treatment in the preceding 3 months, previous immunosuppressive therapy with cytotoxic activity or lymphoid irradiation, as well as pregnancy or lactation. Signed informed consent was obtained from patients, and the study was approved by the Ethical Committee of the Lady Davis, Carmel Medical Center, Israel and by the Ethical Committee of Sourasky Medical Center, Tel Aviv, Israel.

Sera of 60 patients affected by other neurological disease (OND) were obtained from Genomics Collaborative, MA, USA, or obtained under informed consent from patients admitted to the Neuroimmunology Unit, Carmel Medical Center, Israel

The blood samples were collected in evacuated silicon-coated tubes containing gel for the separation of sera from the blood clot (Estar Technologies, Israel). After coagulation of the blood, serum was separated by centrifugation, collected and kept frozen at −25° C. until use. The laboratory evaluations were conducted in a blind manner in relation to the clinical findings.

Glycan Array

All serum samples were tested using GlycoChip®. The glycans were covalently bound to the surface through a linker, as previously described (Schwarz et al., Glycobiology 13, 749-754, 2003; Dukler and Dotan, WIPO, Vol. WO2002IL0000101, Glycominds Ltd., 2002). Briefly, an oligomer of 1,8-diamino-3,6-dioxaoctan (Sigma, St. Louis, Mo.) was synthesized and coupled to the solid support. Consequently, pNP-saccharide conjugates were reduced by sodium dithionite to pAP-saccharide derivatives and reacted with cyanochloride (Sigma)-activated linker. The following p-nitrophenyl glycans derivatives were spotted on the slides: Glc(α1,2)Glc(α), Glc(α1,3)Glc(α), Glc(α1,6)Glc(α), Glc(α1,4)Glc(α), α-Glc, and α-GlcNAc. The glycans were printed in 6 sub arrays on each slide, 4 spots for each glycan per sub array.

Fluorescent Assay for Specific Glycan-Binding Antibodies using Glass Slide GlycoChip®

An adhesive silicon superstructure attached to the slide after the printing enables to apply 6 different serum samples on each slide simultaneously, 4 patient samples and high and low plasma controls.

Serum samples (diluted 1:80 in TBST containing 1% BSA) were incubated for 1 h on the slides. After washing in TNTT buffer (20 mM Tris-HCl pH 7.2, 2 M NaCl, 0.05% Tween-20, 0.05% Triton X-100) labeling reagents were incubated on the glass slides in a Tecan HS-4800 hybridization system. Biotinylated goat anti-human IgM (1:500) and Alexa-488-labeled streptavidin (1:150; Molecular Probes, OR, USA) were incubated sequentially with washings in between for 1 h at 32° C. in the light-protected and temperature-controlled environment of the hybridization system. Slides were scanned using an Axon 4100 array scanner controlled by GenPix (Axon, Calif., USA). Images were analyzed using ArrayPro Analyzer 4.5.1.48 (Media Cybernetics, CA, USA).

Higher Levels of Anti-Glc(α1,2)Glc(α), Glc(α1,3)Glc(α), and Glc(α1,6)Glc(α) IgM Antibodies are Found in Sera of MS Patients vs OND Patients

The level of all anti-glycan IgM antibodies tested was significantly higher in MS patients vs. patients with other neurological diseases. Descriptive statistics for anti-glycan IgM antibody levels in 115 multiple sclerosis patients and 60 patients with other neurological diseases are shown in Table 1. The RFU signals were Log10 transformed for achieving normal distribution. The Receiver Operator Characteristic (ROC) curve for differentiation between MS and OND patients is shown in FIG. 1. The results show that all the glycans can differentiate between MS patients and OND patients with 90% specificity and 40-60% sensitivity. Using cutoff values for 90% specificity, it was determined for each of the patients whether the patient was positive (above cutoff) or negative (below cutoff) for each of the antigens tested. TABLE 1 Mean Log10(RFU), (SD) Sugar Antigen MS OND Glc(α1, 2)Glc(α) 0.51 (0.19) 0.32 (0.21) * Glc(α1, 3)Glc(α) 0.45 (0.20) 0.31 (0.23) * Glc(α1, 6)Glc(α) 0.48 (0.21) 0.20 (0.23) * Glc(α1, 4)Glc(α) 0.52 (0.20) 0.32 (0.25) * α-Glc 0.37 (0.18) 0.17 (0.20) * α-GlcNAc 0.61 (0.25) 0.33 (0.24) * SD, standard deviation; MS, multiple sclerosis; OND, other neurological diseases, RFU, relative fluorescence units. * p < 0.000001 versus MS

Levels of anti-glycan antibodies Glc(α1,4)Glc(α)-Ga4Ga, α-Glc-Ga, α-GlcNAc-GNa, Glc(α1,2)Glc(α)-Ga2Ga, Glc(α1,3Glc(α)-Ga3Ga, Glc(α1,6)Glc(α)-Ga6Ga were determined for each patient. The results are shown in Table 2. Gray patterned cells in Table represent antibodies level above cutoff for 90% specificity. The results show that that there are MS patients who are positive to one or more of Glc(α1,2)Glc(α), Glc(α1,3)Glc(α), or Glc(α1,6)Glc(α) and are not positive for Glc(α1,4)Glc(α), α-Glc, or α-GlcNAc. For example, patient 5138 is positive only for anti Glc(α1,2)Glc(α), patients 5414 and 5415 are positive only for anti Glc(α1,3)Glc(α) and patient 5446 is positive only for anti Glc(α1,6)Glc(α), those patients can be diagnosed only by using only the relevant antigen. Thus, each antigen has a unique specificity contribution for more accurate diagnosis of MS. These results show that anti-Glc(α1,2)Glc(α), Glc(α1,3)Glc(α), and Glc(α1,6)Glc(α) IgM antibodies are found at higher levels in MS patients vs. OND patients. Therefore, these antibodies are useful in the diagnosis of MS patients either by alone or in combination with other antigens. TABLE 2 Patient Ga6- I.D Disease Ga4Ga Ga GNa Ga2Ga Ga3Ga Ga 302 MS 2.09 1.48 1.22 2.01 2.27 2.98 303 MS 1.47 1.26 3.02 2.43 3.40 1.45 308 MS 4.70 3.73 5.35 5.68 2.98 4.26 309 MS 2.06 1.65 1.64 3.41 1.98 1.79 312 MS 1.80 1.34 1.38 3.70 1.69 1.59 313 MS 5.47 3.46 3.03 4.60 6.18 4.55 314 MS 4.30 2.61 6.18 6.74 4.54 4.02 316 MS 3.30 3.12 8.39 3.27 2.61 2.39 318 MS 3.57 2.99 3.41 2.12 2.52 5.70 321 MS 2.24 2.98 5.35 4.49 3.26 2.86 322 MS 4.15 2.78 2.74 3.03 2.77 2.72 323 MS 2.20 1.61 4.46 3.34 2.62 4.61 324 MS 4.45 2.46 7.09 3.95 3.90 2.47 326 MS 3.11 3.61 4.93 11.21  3.22 7.06 328 MS 5.33 2.50 4.32 1.90 2.95 3.43 5086 MS 2.70 1.76 6.60 3.47 3.23 3.31 5089 MS 2.70 2.48 10.78  3.58 2.59 2.70 5090 MS 4.88 2.54 4.72 3.05 3.64 3.21 5093 MS 3.36 3.13 4.30 6.70 2.58 3.97 5098 MS 3.15 1.46 12.01  2.55 2.50 2.21 5103 MS 9.16 4.41 13.14  8.18 5.78 6.95 5106 MS 11.15  3.57 11.36  4.26 7.68 4.17 5110 MS 1.93 1.39 2.13 1.98 1.78 2.52 5111 MS 4.39 2.37 3.22 4.82 3.11 4.58 5113 MS 1.89 1.44 2.94 3.13 1.97 1.69 5119 MS 2.37 2.12 4.09 2.27 3.04 3.06 5120 MS

2.48

5121 MS 4.81 1.90

3.32

5126 MS 3.77 1.97

2.12 3.72 2.93 5129 MS

2.27 4.43

5130 MS 2.97

3.96 1.62 1.87 1.38 5137 MS 3.23 2.44

2.30 2.62 5138 MS 1.94 2.20 3.51

1.88 2.66 5139 MS 2.04 1.35 3.62 1.23 2.81 1.69 5145 MS 1.35 0.76 1.24 1.34 1.27 0.90 5158 MS 2.04 2.21 1.98 1.69 1.75 2.15 5159 MS 4.80

2.96 2.96 2.77

5163 MS 4.17

3.73 2.71

5174 MS 3.83 2.66 3.99 3.18 2.25

5175 MS 1.35 1.19 2.11 1.73 1.62 1.03 5176 MS 3.41

2.69 2.07 2.78 5177 MS

2.58 3.42

2.91 5204 MS 2.59 1.29 2.07 2.01 1.32 2.22 5207 MS 4.49 2.49 2.81 2.17 2.40 2.84 5208 MS 4.97

3.06 2.48

5211 MS 4.58 1.43 3.81 2.78 1.99 1.25 5212 MS 2.02 1.70 4.15 2.54 2.23 2.42 5213 MS 2.30 2.41 3.72 2.05 2.29 3.02 5214 MS 3.39

3.23 2.64 3.12 5215 MS 4.01

3.99 3.02 2.43

5216 MS 9.81 2.35 2.43 3.12 2.94 3.75 5217 MS 3.91 3.00 6.34 3.11 2.15 3.01 5219 MS 2.04 2.76 2.35 4.28 1.42 1.60 5230 MS 5.49 4.02 4.30 5.11 3.19 4.20 5231 MS 2.74 1.98 4.12 5.17 3.69 2.94 5232 MS 4.81 3.77 12.05  4.40 11.63  7.49 5240 MS 4.10 2.30 5.05 3.90 3.32 3.18 5240 MS 4.10 2.30 5.05 3.90 3.32 3.18 5241 MS 2.89 1.68 7.68 6.25 4.50 3.50 5242 MS 5.52 3.08 6.14 4.15 2.69 4.11 5246 MS 2.40 4.76 2.91 1.92 2.12 5.87 5248 MS 1.69 1.01 2.44 2.40 2.37 1.34 5249 MS 2.99 2.15 7.17 3.79 2.76 2.66 5251 MS 1.89 1.23 3.63 2.37 2.81 1.47 5254 MS 2.42 2.35 2.73 1.91 1.68 1.69 5255 MS 8.92 1.53 2.97 2.76 2.84 3.31 5410 MS 2.18 2.93 2.65 1.16 1.38 3.17 5411 MS 1.92 1.42 8.82 1.90 1.69 4.32 5412 MS 2.63 1.72 2.08 2.02 2.17 2.97 5413 MS 7.06 3.84 2.93 3.49 3.79 5.62 5414 MS 2.51 1.50 2.69 4.00 3.95 2.13 5415 MS 3.67 0.91 1.33 2.33 5.02 2.59 5416 MS 4.39 3.81 2.42 1.66 1.76 7.06 5417 MS 5.30 6.53 17.28 4.61 4.08 7.07 5418 MS 3.50 3.04 3.26 3.05 2.56 2.42 5420 MS 2.88 2.28 3.69 3.73 5.83 3.28 5423 MS 2.65 1.72 2.25 4.14 2.19 2.43 5424 MS 3.01 2.20 2.23 2.26 2.53 2.04 5428 MS 2.97 2.38 4.86 2.78 2.78 2.61 5429 MS 3.89 2.49 4.18 8.13 6.00 5.87 5430 MS 3.12 2.31 3.23 4.16 10.76  2.28 5436 MS 6.39 3.49 4.33 3.10 2.15 3.95 5438 MS 2.31 2.18 2.55 2.92 3.52 1.63 5441 MS 2.28 1.83 4.69 7.07 2.82 2.37 5443 MS 1.98 1.44 3.39 1.96 1.65 1.72 5444 MS 9.93 4.45 10.92  7.19 10.55  7.15 5446 MS 2.29 1.66 2.89 3.43 2.89 3.87 5447 MS 1.54 1.79 1.66 4.00 2.27 1.03 5449 MS 6.02 2.59 8.69 5.17 4.51 4.27 5450 MS 1.46 1.12 1.64 1.55 1.25 1.41 5452 MS 1.51 1.03 1.46 1.98 1.60 2.18 5455 MS 3.42 3.15 2.39 1.68 1.32 1.64 5456 MS 3.69 2.42 8.09 2.16 1.15 1.99 5457 MS 7.33 3.88 6.72 6.80 6.62 9.37 5458 MS 2.70 1.46 3.07 4.00 2.04 1.58 5460 MS 2.96 2.39 11.87  2.98 2.00 2.91 5461 MS 2.54 1.69 2.14 3.67 2.49 2.53 5462 MS 2.66 1.54 2.64 3.60 2.11 1.75 5463 MS 5.56 5.11 2.69 2.81 2.02 6.63 5464 MS 2.87 4.94 4.60 3.35 2.90 4.17 5465 MS 3.30 2.28 4.12 4.35 2.91 2.31 5467 MS 4.45 4.12 8.18 3.34 3.83 3.18 5468 MS 2.97 4.81 2.98 4.77 3.27 9.22 5469 MS 5.98 3.49 12.13  6.41 4.21 6.56 5470 MS 3.74 1.91 2.56 4.03 2.43 3.65 5474 MS 2.38 1.32 8.74 1.61 1.99 1.24 5476 MS 2.90 1.90 8.02 3.17 3.05 3.42 5480 MS 2.63 1.95 4.92 3.44 2.86 6.04 5482 MS 3.15 2.27 1.68 1.99 2.67 2.29 5483 MS 6.70 5.78 5.27 6.72 9.71 5.43 5484 MS 3.57 2.92 6.31 5.50 3.56 3.57 5485 MS 8.44 3.22 6.95 3.52 3.36 6.29 5486 MS 3.72 3.93 14.71  3.60 2.90 2.70 5489 MS 1.96 1.23 2.37 1.54 1.23 1.38 9999 MS 3.46 2.79 1.86 2.84 2.00 3.46 5184 OND 2.47 1.06 3.08 2.25 1.47 1.91 5185 OND 0.97 0.81 1.39 0.88 1.01 0.80 5186 OND 1.67 2.09 2.69 3.29 2.03 1.12 5187 OND 1.55 1.82 2.61 1.41 1.40 1.24 5188 OND 6.94 1.99 2.77 2.01 1.74 1.82 5190 OND 1.02 0.61 0.70 1.13 1.13 0.73 5191 OND 1.10 0.77 2.20 1.74 1.32 1.00 5192 OND 0.86 0.98 1.71 1.73 1.47 0.92 5193 OND 1.62 1.25 1.31 0.83 0.76 0.79 5194 OND 2.25 1.40 2.22 1.43 1.89 1.29 5195 OND 3.58 2.69 4.77 2.63 1.90 2.00 5196 OND 1.85 1.60 4.68 1.62 1.15 2.30 5197 OND 2.18 0.98 0.87 1.78 1.67 1.09 5198 OND 2.01 1.82 2.16 2.62 2.45 1.28 5199 OND 1.36 1.27 0.88 1.21 0.90 0.92 5224 OND 1.02 1.01 0.96 1.17 1.54 0.94 5225 OND

5226 OND 1.63 1.50 1.34 2.22 1.58 0.92 5233 OND 2.07 0.91 0.94 3.43

1.31 5234 OND 0.83 0.69 1.41 1.30 2.46 1.02 5235 OND 1.66 2.08 1.91 1.64 1.31 1.59 5236 OND 2.64 1.57 2.92 2.32 2.89 1.52 5237 OND 3.98 2.09

2.90 5239 OND

2.03 1.62 1.35 1.15 2.38 5244 OND 1.45 1.05 1.63 1.34 1.41 1.11 5400 OND 1.20 0.76 1.42 1.58 1.72 1.27 5401 OND 1.87 1.26 2.26 2.41 2.22 1.03 5402 OND

3.57 2.01 2.29

5404 OND 1.66 1.23 1.70 1.67 1.72 1.31 5405 OND 1.61 0.78 1.65 1.31 1.48 1.09 5406 OND 1.22 1.13 1.20 1.48 1.39 1.15 5407 OND 2.56 1.86 4.59 2.92 2.41 2.45 5408 OND

2.70

5706 OND 1.56 1.07 2.39 2.05 1.53 1.02 5707 OND 2.30 1.42 2.37 2.05 2.06 1.40 5708 OND 2.21 1.45 2.96 2.29 2.20 2.03 5709 OND 2.38 1.94 2.60 2.01 2.04 2.11 5710 OND 4.22 2.55 1.87 2.47 3.33

5711 OND 1.13 0.88 0.89 1.02 1.16 0.99 5712 OND 0.83 0.93 1.25 1.25 1.14 0.66 5719 OND 2.29 2.21 2.99 3.26 3.29 1.73 5720 OND 1.22 1.39 1.25 2.58 2.12 1.45 5721 OND 1.74 1.02 1.11 1.46 1.92 1.45 5725 OND 1.90 1.81 1.60 2.12 2.23 2.16 5726 OND

5727 OND

5728 OND 1.28 0.93 1.38 1.54 1.26 0.98 5729 OND 2.85 0.98 2.96 1.69 1.73 1.97 5730 OND 1.50 1.18 1.59 1.56 1.89 1.13 5731 OND 2.01 1.66 1.57 2.05 2.24 1.88 5732 OND 1.65 1.37 2.20 3.20 2.79 1.31 5733 OND 4.02 2.51 4.41

2.78 1.35 5734 OND 1.38 1.05 1.55 1.29 1.19 1.29 5737 OND 1.83 1.76 4.04 2.38 1.98 1.56 5739 OND 2.56 1.91 3.92 3.14 3.30 2.25 5740 OND 2.16 1.59 3.26 2.13 1.81 2.29 5741 OND 2.26 1.54 2.71 1.77 2.53 3.29 6102 OND 4.96

8189 OND 1.76 1.16 2.33 1.82 1.79 1.51 8191 OND 1.62 1.57 2.79 3.87 3.06 2.22

Other Embodiments

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

1. A method of diagnosing multiple sclerosis in a subject, the method comprising providing a test sample from a subject; detecting in said test sample an antibody selected from the group consisting of an anti-Glc(α1,2)Glc(α) antibody, an anti-Glc(α1,3)Glc(α) antibody and an anti-Glc(α1,6)Glc(α) antibody; and comparing the levels of said antibodies in said test sample to a control sample, wherein said control sample is selected from the group consisting of one or more individuals that have multiple sclerosis symptoms and have a known multiple sclerosis status, and one or more individuals that do not show multiple sclerosis symptoms thereby diagnosing multiple sclerosis in said subject.
 2. The method of claim 1, wherein said method comprises detecting an anti-Glc(α1,2)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 3. The method of claim 1, wherein said method comprises detecting an anti-Glc(α1,3)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 4. The method of claim 1, wherein said method comprises detecting an anti-Glc(α1,6)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 5. The method of claim 1, wherein said method further comprises detecting a second antibody selected from the group consisting of an anti α-Glc antibody, an anti-Glc(α1,4)Glc(α) antibody, an anti α-GlcNAc antibody, an anti β-GlcNAc antibody, an anti-Glc(α1,4)Glc(β) antibody an anti β-Glc antibody, an anti β-Gal antibody, an anti-Glc(β1,4)Glc(β1,4)Glc(β) antibody, an anti-GlcNAc(β,1,4)GlcNAc(β) antibody, an anti α-L-Araf antibody, an anti α-L-Rha antibody, an anti-Gal(β1,3)[GlcNAc(β1,6)]GalNAc(α) antibody, an anti-Gal(β1,4)GlcNAc(α) antibody, an anti-Gal(β1,3)GalNAc(α), an anti-Gal(β1,3)GlcNAc(β), an anti β-GlcA antibody and an anti α-Xyl antibody; and comparing the levels of the second antibody in said test sample to the levels of the second antibody in a control sample, wherein said control sample is selected from the group consisting of one or more individuals that have multiple sclerosis symptoms and have a known multiple sclerosis status, and one or more individuals that do not show multiple sclerosis symptoms, thereby diagnosing multiple sclerosis in said subject.
 6. The method of claim 5, wherein the second antibody is an anti (x-Glc antibody, anti-Glc(α1,4)Glc(α) antibody or an anti α-GlcNAc antibody.
 7. The method of claim 1, wherein said control sample consists essentially of a population of one or more individuals that have multiple sclerosis symptoms with a known multiple sclerosis status.
 8. The method of claim 1, wherein said control sample consists essentially of a population of one or more individuals that have an autoimmune disease other than multiple sclerosis.
 9. The method of claim 1, wherein said control sample consists essentially of a population of one or more individuals that have a neurological disease other than multiple sclerosis.
 10. The method of claim 1, wherein said test sample is a biological fluid.
 11. The method of claim 10, wherein said biological fluid is whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva.
 12. The method of claim 11, wherein said biological fluid is serum.
 13. The method of claim 1, wherein said subject is a female.
 14. The method of claim 1, wherein said subject is a male.
 15. The method of claim 1, wherein said at least one antibody is an IgM type antibody.
 16. The method of claim 1, wherein said at least one antibody is an IgG type antibody.
 17. The method of claim 1, wherein said at least one antibody is an IgA type antibody.
 18. The method of claim 5, wherein said anti α-Glc antibody is an IgM type antibody.
 19. The method of claim 5, wherein said anti-Glc(α1,4)Glc(α) antibody is an IgM type antibody.
 20. The method of claim 5, wherein said anti α-GlcNAc antibody is an IgM type antibody.
 21. The method of claim 1, wherein said diagnosis is an early diagnosis of multiple sclerosis.
 22. The method of claim 1, wherein said control sample is determined using an Expanded Disability Status Scale (EDSS) assessment or a Magnetic Resonance Imaging (MRI) assessment.
 23. The method of claim 1, wherein said control sample is determined using an Expanded Disability Status Scale (EDSS) assessment.
 24. The method of claim 1, wherein said method comprises detecting at least two of said antibodies.
 25. The method of claim 1, wherein said method comprises detecting at least four of said antibodies.
 26. The method of claim 1, wherein said method comprises detecting at least six of said antibodies.
 27. A method of diagnosing a multiple sclerosis exacerbation in a subject, the method comprising providing a test sample from a subject; detecting an antibody selected from the group consisting of an anti-Glc(α1,2)Glc(α) antibody, an anti-Glc(α13,)Glc(α) antibody and an anti-Glc(α1,6)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to levels of said antibody in a control sample, wherein said control sample is derived from one or more individuals whose multiple sclerosis exacerbation status is known, thereby diagnosing multiple sclerosis exacerbation in said subject.
 28. The method of claim 27, wherein said method comprises detecting an anti-Glc(α1,2)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 29. The method of claim 27, wherein said method comprises detecting an anti-Glc(α1,3)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 30. The method of claim 27, wherein said method comprises detecting an anti-Glc(α1,6)Glc(α) type antibody in said test sample; and comparing the levels of said antibodies in said test sample to said control sample.
 31. The method of claim 27, wherein said method comprises detecting two of said antibodies in said sample.
 32. The method of claim 27, wherein said method comprises detecting three of said antibodies in said sample.
 33. The method of claim 27, wherein said control sample consists essentially of a population of one or more individuals in remission multiple sclerosis status that do not show symptoms of a multiple sclerosis exacerbation, and a multiple sclerosis exacerbation is diagnosed in said subject if more of said antibody is present in said test sample than in said control sample.
 34. The method of claim 27, wherein said control sample consists essentially of a population of one or more individuals that their multiple sclerosis status in exacerbation, and show symptoms of a multiple sclerosis exacerbation, and a multiple sclerosis exacerbation is diagnosed in said subject if similar levels of said antibody are present in said test sample and in said control sample.
 35. The method of claim 27, wherein said test sample is a biological fluid.
 36. The method of claim 30, wherein said biological fluid is whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva.
 37. The method of claim 30, wherein said biological fluid is serum.
 38. The method of claim 27, wherein said subject is a female.
 39. The method of claim 27, wherein said subject is a male.
 40. The method of claim 27, wherein said diagnosis is an early diagnosis of multiple sclerosis exacerbation.
 41. The method of claim 27, wherein said subject has been treated by subcutaneous administration of interferon beta.
 42. The method of claim 27, wherein said subject has been treated by subcutaneous administration of glitamerer acetate.
 43. A method for assessing multiple sclerosis disease activity in a subject, the method comprising providing a test sample from a subject; detecting an antibody selected from the group consisting of an anti-Glc(α1,2)Glc(α) antibody, an anti-Glc(α1,3)Glc(α) antibody and an anti-Glc(α1,6)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to levels of said antibody in a control sample, wherein said control sample is derived from one or more individuals whose multiple sclerosis disease activity is known, thereby assessing multiple sclerosis activity in said subject.
 44. The method of claim 43, wherein said method comprises detecting an anti-Glc(α1,2)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 45. The method of claim 43, wherein said method comprises detecting an anti-Glc(α1,3)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 46. The method of claim 43, wherein said method comprises detecting an anti-Glc(α1,6)Glc(α) type antibody in said test sample; and comparing the levels of said antibodies in said test sample to said control sample.
 47. The method of claim 43, wherein said method comprises detecting two of said antibodies in said sample.
 48. The method of claim 43, wherein said method comprises detecting three of said antibodies in said sample.
 49. The method of claim 43, wherein said control sample consists essentially of a population of one or more individuals in remission multiple sclerosis status that do not show symptoms of a multiple sclerosis exacerbation, and a multiple sclerosis exacerbation is diagnosed in said subject if more of said antibody is present in said test sample than in said control sample.
 50. The method of claim 43, wherein said control sample consists essentially of a population of one or more individuals that their multiple sclerosis status in exacerbation, and show symptoms of a multiple sclerosis exacerbation, and a multiple sclerosis exacerbation is diagnosed in said subject if similar levels of said antibody are present in said test sample and in said control sample.
 51. The method of claim 43, wherein said test sample is a biological fluid.
 52. The method of claim 51, wherein said biological fluid is whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva.
 53. The method of claim 51, wherein said biological fluid is serum.
 54. The method of claim 43, wherein said subject is a female.
 55. The method of claim 43, wherein said subject is a male.
 56. The method of claim 43, wherein said diagnosis is an early diagnosis of multiple sclerosis exacerbation.
 57. The method of claim 43, wherein said subject has been treated by subcutaneous administration of interferon beta.
 58. The method of claim 43, wherein said subject has been treated by subcutaneous administration of glitamerer acetate.
 59. A method of predicting whether patients with a CIS suggestive of MS or newly diagnosed relapsing remitting MS will have a highly active disease progression, the method comprising providing a test sample from a subject; detecting an antibody selected from the group consisting of an anti-Glc(α1,2)Glc(α) antibody, an anti-Glc(α1,3)Glc(α) antibody and an anti-Glc(α1,6)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to levels of said antibody in a control sample, wherein said control sample is derived from one or more individuals whose multiple sclerosis disease progression is known, thereby determining disease progression in said patient.
 60. The method of claim 59, wherein said method comprises detecting an anti-Glc(α1,2)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 61. The method of claim 59, wherein said method comprises detecting an anti-Glc(α1,3)Glc(α) antibody in said test sample; and comparing the levels of said antibody in said test sample to said control sample.
 62. The method of claim 59, wherein said method comprises detecting an anti-Glc(α1,6)Glc(α) type antibody in said test sample; and comparing the levels of said antibodies in said test sample to said control sample.
 63. The method of claim 59, wherein said method comprises detecting two of said antibodies in said sample.
 64. The method of claim 59, wherein said method comprises detecting three of said antibodies in said sample.
 65. The method of claim 59, wherein said control sample consists essentially of a population of one or more individuals in remission multiple sclerosis status that do not show symptoms of a multiple sclerosis exacerbation, and a multiple sclerosis exacerbation is diagnosed in said subject if more of said antibody is present in said test sample than in said control sample.
 66. The method of claim 59, wherein said test sample is a biological fluid.
 67. The method of claim 66, wherein said biological fluid is whole blood, serum, plasma, spinal cord fluid, urine, tears or saliva.
 68. The method of claim 66, wherein said biological fluid is serum.
 69. The method of claim 59, wherein said subject is a female.
 70. The method of claim 59, wherein said subject is a male.
 71. A kit for diagnosing symptoms associated with, determining the prognosis of, or assessing the activity of, multiple sclerosis in subject, the kit comprising: a first reagent that specifically detects an anti-Glc(α1,2)Glc(α) antibody or anti-Glc(α1,3)Glc(α) antibody or anti-Glc(α1,6)Glc(α) antibody; a second reagent that specifically detects a second antibody selected from the group consisting of an anti α-Glc antibody, an anti-Glc(β1,4)Glc(α) antibody, an anti α-GlcNAc antibody, an anti β-GlcNAc antibody, an anti-Glc(α1,4)Glc(β) antibody an anti β-Glc antibody, an anti β-Gal antibody, an anti-Glc(β1,4)Glc(β1,4)Glc(β) antibody, an anti-GlcNAc(β,1,4)GlcNAc(β) antibody, an anti α-L-Araf antibody, an anti α-L-Rha antibody, an anti-Gal(β1,3)[GlcNAc(β1,6)]GalNAc(α) antibody, an anti-Gal(β1,4)GlcNAc(α) antibody, an anti-Gal(β1,3)GalNAc(α), an anti-Gal(β1,3)GlcNAc(β), an anti β-GlcA antibody and an anti α-Xyl antibody; and directions for using said kit.
 72. The kit of claim 71, wherein said first reagent specifically detects an anti-Glc(α1,2)Glc(α) antibody.
 73. The kit of claim 71, wherein said first reagent specifically detects an anti-Glc(α1,3)Glc(α) antibody.
 74. The kit of claim 71, wherein said first reagent specifically detects an anti-Glc(α1,6)Glc(a) antibody.
 75. The kit of claim 71, further comprising a reagent that specifically detects an IgM type antibody. 